The key to this detection is how to distinguish professional malicious users from normal users. The intuitive idea is to maximize the distance between malicious and normal users with clustering. In this paper, we utilize Metric Learning, a popular theory that is widely applied in clustering, embedding, and image recognition. The paradigm of metric learning is to estimate a proper “distance” between entities to measure the relationships [1]}. Consider learning a distance metric matrix \(A\)  as follows:
\(\begin{aligned}{d_\text{A}}({p_j},{p_k}) = {\left\Vert  {{p_j} - {p_k}} \right\Vert _\text{A}} = \sqrt{{{({p_j} - {p_k})}^T}c^2A({p_j} - {p_k})},\end{aligned}\) 
